The crop season is upon us and producers across the state are planting and getting their crops established. Farmers are interested in knowing what works best, yields the most, and especially, what is most profitable during these tight economic times. Some may want to compare products or practices on their own farm or look at information from other farms or industry studies.

How should a basic study be set up or laid out in the field? One very common approach is to divide a field in half and compare the halves or possibly compare two fields in close proximity and see which variety or practice yields highest. This approach can end with very misleading results because of the variability that exists across a field or fields due to many factors. Sources of variability include:

variations in soil type,

topography,

varying management practices,

drainage,

pesticide residues,

disease pressure,

compaction, and

weather events.

This is the first of a four-part series on agricultural research and interpretation by university extension educators in the North Central Region Advanced Crops Academy. Also see:

Just as you can count on yield monitor results to not remain constant across a field, you can essentially count on there being sources of variability that would impact study results if you just split a field in half or compared fields across the road from each other.

A better approach, which provides a better estimate of future performance of a treatment, is to put out replicated studies with random placement of treatments in each replication. This simply means that the same treatment is put out more than one time across the area of study to be assured that treatment performance is not based on location in the field. Replication from three to six times is common in most agricultural studies. The more replications, the more reliable results will be in a given comparison. Repeating the replicated comparisons for more than one year is also a good idea to test performance over more environments. This will provide stronger conclusions and estimates of real differences between treatments.

As an example, an on-farm trial completed in 2016 is described below, showing how replication affected the results. This study compared two systems commonly used in planting pinto beans in Nebraska. The treatments were applied and replicated six times with random placement. One treatment was pinto beans planted in 30-inch rows at a population of 90,000 plants per acre; the second treatment was pinto beans planted in 7.5-inch rows at 120,000 plants per acre (Figure 1). This was a large field trial with each treatment being 60 feet wide by 1,400 feet long. The randomization was as follows:

Rep 1

Rep 2

Rep 3

Rep 4

Rep 5

Rep 6

7.5”

30”

30”

7.5”

7.5”

30”

7.5”

30”

30”

7.5”

30”

7.5”

Average yields from the treatments in the six replications were:

7.5-inch rows with 120,000 population yielded 52 bu/ac

30-inch rows with 90,000 population yielded 44 bu/ac.

The 7.5-inch treatment yielded 8 bu/ac more than the 30-inch treatment. Having analyzed the yield data statistically (at the .05 probability level), yields were significantly different, with the least significant difference being 2 bu/ac. This means that due to variability within the study, a yield difference of less than 2 bu/ac would not indicate any treatment differences.

During early August a hail storm damaged the field, with the most significant damage occurring on the half of the field containing replications 4, 5 and 6. If the field had just been split with one treatment on each side, results would have looked different. If we lump the 7.5-inch treatments from the hailed side of the field together we would find an average yield of 49 bu/ac. In comparison, if we lumped the 30-inch treatments together on the side with minimal hail, average yield for this treatment would have been 45 bu/ac. This equals a difference between treatments of 4 bu/ac (half the difference that was detected by the full, replicated trial). Conversely, if we had the 30-inch treatments on the side of the field that received the most hail, yield for this treatment would have been 43 bu/ac and yield for the 7.5-inch treatment on the side receiving minimal hail would have been 54 bu/ac, for a difference of 11 bu/ac (Figure 2).

It is clear that when the six replications were spread out across the field, we found a more accurate estimate of the impact of these systems on yield than when splitting the field in half. In all three layouts the 7.5-inch treatment yielded the most. The split field design either exaggerated or diminished the yield advantage of the 7.5-inch treatment, depending on which treatment was exposed to the heavier hail damage (Figure 2).

Figure 2. Change in yield advantage of the 7.5-inch treatment as compared in split field layout versus a replicated randomized field layout. An early August hailstorm caused greater damage on one-half of the field. Like treatments were lumped together on the hailed half versus the light-hailed half to get the above average yields in the split field comparisons.

Poorly laid out field studies can generate misleading data and can lead to incorrect conclusions. Consider this when looking at data from other studies. In our modern era with GPS guidance, it is relatively easy to put in replicated, randomized studies, even on large field-scale comparisons.